code: def __call__(self, chunk_v, chunk_a):
      if chunk_v.size(0) < self.segment_length:
          chunk_v = torch.nn.functional.pad(
              chunk_v,
              (
                  0,
                  0,
                  0,
                  0,
                  0,
                  0,
                  0,
                  self.segment_length - chunk_v.size(0),
              ),
          )
      if chunk_a.size(0) < self.segment_length * 640:
          chunk_a = torch.nn.functional.pad(
              chunk_a,
              (0, 0, 0, self.segment_length * 640 - chunk_a.size(0)),
          )
  
      if self.context_length == 0:
          return chunk_v.float(), chunk_a.float()
      else:
          chunk_with_context_v = torch.cat((self.context_v, chunk_v))
          chunk_with_context_a = torch.cat((self.context_a, chunk_a))
          self.context_v = chunk_v[-self.context_length_v :]
          self.context_a = chunk_a[-self.context_length_a :]
          return (
              chunk_with_context_v.float(),
              chunk_with_context_a.float(),
          )
  
imports: import numpy
  import sentencepiece
  import torch
  import torchaudio
  import torchvision
  import sys
  from avsr.data_prep.detectors.mediapipe.detector import LandmarksDetector
  from avsr.data_prep.detectors.mediapipe.video_process import VideoProcess
  from torchaudio.utils import download_asset
label_desc: Use image node_id 2 for calling the ContextCacher obj's underlying member method code with example usage: obj.__call__(chunk_v, chunk_a) and returns: chunk_v,  chunk_a, chunk_with_context_v,  chunk_with_context_a